Large - Scale Non - Linear Regression within the Mapreduce Framework

Large-scale Non-linear Regression within the MapReduce Framework By: Ahmed Khademzadeh Thesis Advisor: Philip Chan, Ph.D. Regression models have many applications in real world problems such as finance, epidemiology, environmental science, etc.. Big datasets are everywhere these days, and bigger datasets would help us to construct better models from the data. The issue with big datasets is that they would need a long time to be processed or even to be read on a single machine. This research employs MapReduce to model large-scale non-linear regression problems in a parallel fashion. MRRT (MapReduce Regression Tree) algorithm divides the feature space into overlapping subspaces and then shuffles each of the subspace’s data items to a node in the cluster. Each node in the cluster then constructs a regression tree for the subspace of the data it has received. Different versions of algorithm (overlapping/non-overlapping subspaces and weighted/unweighted prediction using neighboring models) are proposed and compared with the regression tree (RT) algorithm implemented in Matlab libraries. Experiments on synthetic and real datasets show that MRRT algorithm that is devised to be fast and scalable for MapReduce framework not only has a close to linear speedup, and close to optimum scalability, but also outperforms the RT algorithm in terms of accuracy (in most cases) and improves the prediction time by more than 80%. Although MRRT is designed for MapReduce framework, it could be used on a single machine, and in that case it improves the learning time by 60% (in most cases) comparing to RT algorithm, and shows to be of close to linear scalability (comparing to RT algorithm which is roughly of quadratic scalability).